The common approach to semiconductor process control is based on physical measurements of a certain number of processed wafers. Therefore, the status of the process is just controlled within certain time lags, which might result in delayed observations of process abnormalities. Moreover, the measurement rules are typically of a statical nature meaning that the measurement policy is not adapted to current production conditions. In this master thesis, we propose a novel approach to semiconductor process control based on virtual measurements. These are predictions on the location of the real measurements in the control chart depending on the status of the equipment while processing. The advantage of the application of virtual measurements is that they are available for every wafer. Therefore, the status of the process can be steadily controlled and process deviations can be realized earlier. Moreover, the usage of virtual measurements allows for adapting the measurement policy to current process conditions. Whenever virtual measurements provide sufficiently strong evidence that the process is running within the control limits, there is no need of triggering physical measurements. However, if process abnormalities are signalized by the virtual measurement, a physical measurement needs to be triggered. Therefore, physical metrology operations can be scheduled in a more efficient way.

1. On a Sampling Decision System using
Virtual Metrology
D. Kurz
O. Univ.-Prof. Dr. J. Pilz
AAU Klagenfurt, Department of Statistics
Dr. C. De Luca
Infineon Technologies Austria AG
4 December 2013
D. Kurz
On a Sampling Decision System usingVirtual Metrology

2. Innovation
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Semiconductor process control: the current state-of-the-art
Based on physical measurements
Only certain number of wafers measured
Statical measurement rules
UCL
Semiconductor process control with a sampling decision
system
Based on virtual measurements
Virtual measurements for every wafer
Target
Dynamical measurement rules
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LCL
D. Kurz
On a Sampling Decision System usingVirtual Metrology

3. 2.0
Applied statistical methods
Expected utility of measurement information
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VM: N(0,10²)
VM: N(0,15²)
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Decision-theoretical modeling of virtual
measurements
Density
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Sampling decision evaluation
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PIT
Lindley information and wafer quality risk values
Monitoring reliability of virtual measurements
Quantification of performance of virtual
measurements
Detection of unreliable virtual measurements
Update of precision of virtual measurements
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D. Kurz
On a Sampling Decision System usingVirtual Metrology
1.0

4. 50
real metrology
virtual metrology
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wafer no.
D. Kurz
On a Sampling Decision System usingVirtual Metrology